Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows210
Duplicate rows (%)2.1%
Total size in memory488.4 KiB
Average record size in memory50.0 B

Variable types

Numeric4
Categorical14

Alerts

Dataset has 210 (2.1%) duplicate rowsDuplicates
para4 is highly correlated with priceHigh correlation
price is highly correlated with para4High correlation
para2 is highly correlated with priceHigh correlation
para4 is highly correlated with priceHigh correlation
price is highly correlated with para2 and 1 other fieldsHigh correlation
para2 is highly correlated with priceHigh correlation
price is highly correlated with para2High correlation
para1 is highly skewed (γ1 = 88.1990368) Skewed
para1 has 836 (8.4%) zeros Zeros

Reproduction

Analysis started2021-10-14 06:40:00.328688
Analysis finished2021-10-14 06:40:30.581095
Duration30.25 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

para1
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3808
Minimum0
Maximum337
Zeros836
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size88.0 KiB
2021-10-14T01:40:30.998384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum337
Range337
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.500830893
Coefficient of variation (CV)2.535364204
Kurtosis8450.146622
Mean1.3808
Median Absolute Deviation (MAD)0
Skewness88.1990368
Sum13808
Variance12.25581694
MonotonicityNot monotonic
2021-10-14T01:40:31.236564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
17052
70.5%
31613
 
16.1%
0836
 
8.4%
2268
 
2.7%
4164
 
1.6%
551
 
0.5%
66
 
0.1%
73
 
< 0.1%
92
 
< 0.1%
132
 
< 0.1%
Other values (3)3
 
< 0.1%
ValueCountFrequency (%)
0836
 
8.4%
17052
70.5%
2268
 
2.7%
31613
 
16.1%
4164
 
1.6%
551
 
0.5%
66
 
0.1%
73
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
3371
 
< 0.1%
241
 
< 0.1%
132
 
< 0.1%
92
 
< 0.1%
81
 
< 0.1%
73
 
< 0.1%
66
 
0.1%
551
 
0.5%
4164
 
1.6%
31613
16.1%

para2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1016
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean447.384
Minimum16
Maximum2554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.0 KiB
2021-10-14T01:40:31.548947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile124
Q1301
median434
Q3582
95-th percentile775
Maximum2554
Range2538
Interquartile range (IQR)281

Descriptive statistics

Standard deviation221.0058612
Coefficient of variation (CV)0.4939958989
Kurtosis8.07765682
Mean447.384
Median Absolute Deviation (MAD)140
Skewness1.373929782
Sum4473840
Variance48843.5907
MonotonicityNot monotonic
2021-10-14T01:40:31.935464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1693
 
0.9%
55473
 
0.7%
21559
 
0.6%
50954
 
0.5%
34952
 
0.5%
30649
 
0.5%
42040
 
0.4%
32237
 
0.4%
23537
 
0.4%
34736
 
0.4%
Other values (1006)9470
94.7%
ValueCountFrequency (%)
1693
0.9%
176
 
0.1%
189
 
0.1%
1911
 
0.1%
206
 
0.1%
218
 
0.1%
226
 
0.1%
2313
 
0.1%
246
 
0.1%
2515
 
0.1%
ValueCountFrequency (%)
25541
< 0.1%
25141
< 0.1%
25031
< 0.1%
24531
< 0.1%
23321
< 0.1%
23251
< 0.1%
23241
< 0.1%
23171
< 0.1%
22931
< 0.1%
21911
< 0.1%

para4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct243
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.459522
Minimum1
Maximum27.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.0 KiB
2021-10-14T01:40:32.574640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7.2
Q313.6
95-th percentile13.6
Maximum27.2
Range26.2
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation4.613526048
Coefficient of variation (CV)0.545364862
Kurtosis-1.203979137
Mean8.459522
Median Absolute Deviation (MAD)4.4
Skewness0.1146327091
Sum84595.22
Variance21.28462259
MonotonicityNot monotonic
2021-10-14T01:40:33.105832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.63306
33.1%
6.8437
 
4.4%
4426
 
4.3%
6350
 
3.5%
2278
 
2.8%
3226
 
2.3%
3.2210
 
2.1%
2.4207
 
2.1%
3.6197
 
2.0%
4.4190
 
1.9%
Other values (233)4173
41.7%
ValueCountFrequency (%)
138
0.4%
1.011
 
< 0.1%
1.17
 
0.1%
1.161
 
< 0.1%
1.290
0.9%
1.252
 
< 0.1%
1.35
 
0.1%
1.332
 
< 0.1%
1.352
 
< 0.1%
1.49
 
0.1%
ValueCountFrequency (%)
27.25
0.1%
26.45
0.1%
262
 
< 0.1%
25.62
 
< 0.1%
24.82
 
< 0.1%
242
 
< 0.1%
23.21
 
< 0.1%
22.82
 
< 0.1%
22.42
 
< 0.1%
21.63
< 0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct932
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.733056
Minimum50.73
Maximum5700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.0 KiB
2021-10-14T01:40:33.542478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50.73
5-th percentile150
Q1250
median370
Q3550
95-th percentile880
Maximum5700
Range5649.27
Interquartile range (IQR)300

Descriptive statistics

Standard deviation277.4359467
Coefficient of variation (CV)0.6396467663
Kurtosis33.78328466
Mean433.733056
Median Absolute Deviation (MAD)130
Skewness3.619454796
Sum4337330.56
Variance76970.70453
MonotonicityNot monotonic
2021-10-14T01:40:33.948673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400365
 
3.6%
300314
 
3.1%
350290
 
2.9%
250282
 
2.8%
500243
 
2.4%
200235
 
2.4%
450232
 
2.3%
550188
 
1.9%
600158
 
1.6%
650150
 
1.5%
Other values (922)7543
75.4%
ValueCountFrequency (%)
50.731
 
< 0.1%
50.881
 
< 0.1%
51.921
 
< 0.1%
52.31
 
< 0.1%
52.481
 
< 0.1%
54.331
 
< 0.1%
57.721
 
< 0.1%
604
< 0.1%
61.031
 
< 0.1%
61.711
 
< 0.1%
ValueCountFrequency (%)
57001
< 0.1%
46001
< 0.1%
38001
< 0.1%
35901
< 0.1%
35501
< 0.1%
35001
< 0.1%
31701
< 0.1%
30801
< 0.1%
30501
< 0.1%
29001
< 0.1%

loc1_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8514 
1
1486 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08514
85.1%
11486
 
14.9%

Length

2021-10-14T01:40:34.276028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:34.570757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08514
85.1%
11486
 
14.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8777 
1
1223 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08777
87.8%
11223
 
12.2%

Length

2021-10-14T01:40:34.851994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:35.022402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08777
87.8%
11223
 
12.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8393 
1
1607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08393
83.9%
11607
 
16.1%

Length

2021-10-14T01:40:35.289614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:35.444195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08393
83.9%
11607
 
16.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9055 
1
945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09055
90.5%
1945
 
9.4%

Length

2021-10-14T01:40:35.677846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:35.864827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
09055
90.5%
1945
 
9.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9227 
1
 
773

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
09227
92.3%
1773
 
7.7%

Length

2021-10-14T01:40:36.051203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:36.271854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
09227
92.3%
1773
 
7.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9154 
1
 
846

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
09154
91.5%
1846
 
8.5%

Length

2021-10-14T01:40:36.457564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:36.629058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
09154
91.5%
1846
 
8.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9380 
1
 
620

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09380
93.8%
1620
 
6.2%

Length

2021-10-14T01:40:36.848436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:37.020181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
09380
93.8%
1620
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8919 
1
1081 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08919
89.2%
11081
 
10.8%

Length

2021-10-14T01:40:37.221237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:37.438588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08919
89.2%
11081
 
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9273 
1
 
727

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09273
92.7%
1727
 
7.3%

Length

2021-10-14T01:40:37.623127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:37.780608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
09273
92.7%
1727
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

loc1_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9310 
1
 
690

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09310
93.1%
1690
 
6.9%

Length

2021-10-14T01:40:37.920659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:38.077362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
09310
93.1%
1690
 
6.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dow_Mon
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8082 
1
1918 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
08082
80.8%
11918
 
19.2%

Length

2021-10-14T01:40:38.203061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:38.342399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08082
80.8%
11918
 
19.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dow_Thu
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8092 
1
1908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
08092
80.9%
11908
 
19.1%

Length

2021-10-14T01:40:38.516290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:38.879441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08092
80.9%
11908
 
19.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dow_Tue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8003 
1
1997 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08003
80.0%
11997
 
20.0%

Length

2021-10-14T01:40:39.043858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:39.215516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08003
80.0%
11997
 
20.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dow_Wed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7767 
1
2233 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07767
77.7%
12233
 
22.3%

Length

2021-10-14T01:40:39.388269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T01:40:39.542757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
07767
77.7%
12233
 
22.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-14T01:40:27.625976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:16.845851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:25.169249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:26.409801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:27.944000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:17.911552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:25.487041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:26.727403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:28.251719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:24.509341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:25.793371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:27.026150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:28.559113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:24.853041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:26.108965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-14T01:40:27.325119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-14T01:40:39.763676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-14T01:40:40.540465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-14T01:40:41.136272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-14T01:40:41.711777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-14T01:40:42.208613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-14T01:40:29.207487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-14T01:40:30.024991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

para1para2para4priceloc1_0loc1_1loc1_2loc1_3loc1_4loc1_5loc1_6loc1_7loc1_8loc1_9dow_Mondow_Thudow_Tuedow_Wed
016623.873.4910000000001000
113409.2300.000000000010100
20163.0130.010000000001000
31176.4365.000001000001000
4161010.8357.500000100000100
5148313.6550.010000000000010
612143.0210.010000000001000
714523.0366.6700000001000001
811304.5193.3300000000100010
94100413.21400.001000000000000

Last rows

para1para2para4priceloc1_0loc1_1loc1_2loc1_3loc1_4loc1_5loc1_6loc1_7loc1_8loc1_9dow_Mondow_Thudow_Tuedow_Wed
9990159513.6620.000000000010001
9991414313.6190.000010000000100
9992032313.6420.000100000000001
999302914.0175.000100000000100
999416285.2250.000000010000010
9995338612.0460.000000000010000
999613868.0325.000000001000100
999701905.6133.3310000000000010
9998371713.6820.000000001000000
999916224.8375.000000001000100

Duplicate rows

Most frequently occurring

para1para2para4priceloc1_0loc1_1loc1_2loc1_3loc1_4loc1_5loc1_6loc1_7loc1_8loc1_9dow_Mondow_Thudow_Tuedow_Wed# duplicates
44121513.6670.01000000000001011
184321513.6670.01000000000000010
45121513.6670.0100000000010008
24034913.6460.0010000000000007
43121513.6670.0100000000000017
173172713.6950.0010000000000107
27034913.6460.0010000000001006
39119313.6275.0010000000000106
36115513.6250.0010000000010005
63132213.6430.0001000000000015